Automatic evaluation and improvement of roof segments for modelling missing details using Lidar data

Abstract

Despite the large number of studies conducted during the last three decades concerning 3D building modelling starting from Lidar data, two persistent problems still exist. The first one is the absence of some roof details which will not only disappear in the building roof model due to their small areas regarding the point density, but are also considered as undesirable noise among the modelling procedures. The second problem consists in that the involved segmentation algorithms do not perform well in the presence of noise in the building point cloud data. These two problems generate undesirable deformation in the final 3D building model. This paper proposes a new automatic approach for detecting and modelling the missing roof details in addition to improving the building roof segments. In this context, the error map matrix, which presents the deviations of points to their fitting planes, is considered. Moreover, this matrix is analyzed in order to deduce the mask of missing roof details. At this stage, a new numeric factor is defined for estimating the roof segmentation accuracy in addition to the validity of the roof segmentation result. Then, the building point cloud is enhanced in order to decrease the negative noise influence and, consequently, to improve the building roof segments. Finally, the functionality and the accuracy of the proposed approach are tested and discussed.

Automatic filtering and 2D modeling of airborne laser scanning building point cloud

“…Moreover, there are two types of missing roof planes on building roofs: first, the details having considerable altitude and negligible areas; second, the details having negligible altitude regardless of area value. In this article, the approach suggested by Tarsha Kurdi and Awrangjeb (2020) for modeling the missing planes is employed. This approach allows only modeling the missing roof planes having considerable altitude, since it depends on the height of the missing roof planes in the error map matrix (see Figure 9a later).…”

“…Moreover, its area is equal to two pixels. For more details about this algorithm, see Tarsha Kurdi and Awrangjeb (2020).…”

“…One of the crucial elements of these approaches of modeling accuracy estimation is the calculation of distances (point per point) between the roof planes and the point cloud. There are two main forms to present the accuracy estimation result: vector form (Ostrowski, Pilarska, Charyton, & Bakuła, 2018) and raster form (Tarsha Kurdi & Awrangjeb, 2020). In this context, Tarsha Kurdi and Awrangjeb (2020) suggest the calculation of the SAQ (segmentation accuracy and quality) factor based on the error map matrix (see Figure 9).…”

“…There are two main forms to present the accuracy estimation result: vector form (Ostrowski, Pilarska, Charyton, & Bakuła, 2018) and raster form (Tarsha Kurdi & Awrangjeb, 2020). In this context, Tarsha Kurdi and Awrangjeb (2020) suggest the calculation of the SAQ (segmentation accuracy and quality) factor based on the error map matrix (see Figure 9). Indeed, this matrix expresses the building model accuracy in comparison to the building point cloud.…”

CityJSON Building Generation from Airborne LiDAR 3D Point Clouds

“…Despite this, it is currently rarely used in practice; one of the reasons for this is the lack of publicly available CityJSON models (https://www.cityjson.org/datasets/), which is a consequence of limited CityJSON generation methods. In this research, we propose a new CityJSON generation method, which relies on state-of-the-art building-creation components from LiDAR data [3,8].…”